Pemetaan Cuaca Berdasarkan Penyebaran Awan Menggunakan Analisis Korespondensi
DOI:
https://doi.org/10.55606/jurrimipa.v4i2.6276Keywords:
Atmospheric data visualization, Clouds, Correspondence, Weather mapping, WeatherAbstract
Weather and cloud cover are two fundamental atmospheric elements that are closely interconnected and play a vital role in understanding, classifying, and predicting atmospheric conditions. Accurate classification of weather conditions is essential not only for meteorological studies but also for practical applications in agriculture, aviation, transportation, and disaster management. Among the various statistical techniques available, correspondence analysis has proven to be an effective exploratory method for revealing associations among categorical variables by displaying their relationships in a two-dimensional graphical map. The primary objective of this study is to examine the relationship between different categories of weather conditions and levels of cloud cover using correspondence analysis. The data were sourced from the Kaggle platform, consisting of 13,200 observations with two key categorical variables: weather conditions (sunny, cloudy, rainy, and snowy) and cloud cover (clear, partly cloudy, cloudy, and overcast). This large dataset provides sufficient variability and reliability to capture the complex interdependence between the two variables. The findings reveal a statistically significant relationship between weather categories and cloud cover levels. The correspondence map visualization shows distinct patterns: clear weather is strongly associated with cloudless skies, while rainy and snowy weather conditions are predominantly linked to fully overcast skies. Cloudy and partly cloudy conditions occupy an intermediate position on the correspondence map, reflecting a transitional state between favorable and unfavorable weather. The first dimension of the map represents a spectrum from favorable weather conditions (sunny and clear) to adverse conditions (rainy and snowy), whereas the second dimension primarily reflects the intensity or density of cloud cover, ranging from thin to thick cloud layers. Overall, this study demonstrates the usefulness of correspondence analysis as an exploratory tool for meteorological data, offering valuable insights into the interrelation of weather and cloud cover categories.
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